R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(103.34
+ ,98.60
+ ,96.33
+ ,102.60
+ ,96.90
+ ,96.33
+ ,100.69
+ ,95.10
+ ,95.05
+ ,105.67
+ ,97.00
+ ,96.84
+ ,123.61
+ ,112.70
+ ,96.92
+ ,113.08
+ ,102.90
+ ,97.44
+ ,106.46
+ ,97.40
+ ,97.78
+ ,123.38
+ ,111.40
+ ,97.69
+ ,109.87
+ ,87.40
+ ,96.67
+ ,95.74
+ ,96.80
+ ,98.29
+ ,123.06
+ ,114.10
+ ,98.20
+ ,123.39
+ ,110.30
+ ,98.71
+ ,120.28
+ ,103.90
+ ,98.54
+ ,115.33
+ ,101.60
+ ,98.20
+ ,110.40
+ ,94.60
+ ,100.80
+ ,114.49
+ ,95.90
+ ,101.33
+ ,132.03
+ ,104.70
+ ,101.88
+ ,123.16
+ ,102.80
+ ,101.85
+ ,118.82
+ ,98.10
+ ,102.04
+ ,128.32
+ ,113.90
+ ,102.22
+ ,112.24
+ ,80.90
+ ,102.63
+ ,104.53
+ ,95.70
+ ,102.65
+ ,132.57
+ ,113.20
+ ,102.54
+ ,122.52
+ ,105.90
+ ,102.37
+ ,131.80
+ ,108.80
+ ,102.68
+ ,124.55
+ ,102.30
+ ,102.76
+ ,120.96
+ ,99.00
+ ,102.82
+ ,122.60
+ ,100.70
+ ,103.31
+ ,145.52
+ ,115.50
+ ,103.23
+ ,118.57
+ ,100.70
+ ,103.60
+ ,134.25
+ ,109.90
+ ,103.95
+ ,136.70
+ ,114.60
+ ,103.93
+ ,121.37
+ ,85.40
+ ,104.25
+ ,111.63
+ ,100.50
+ ,104.38
+ ,134.42
+ ,114.80
+ ,104.36
+ ,137.65
+ ,116.50
+ ,104.32
+ ,137.86
+ ,112.90
+ ,104.58
+ ,119.77
+ ,102.00
+ ,104.68
+ ,130.69
+ ,106.00
+ ,104.92
+ ,128.28
+ ,105.30
+ ,105.46
+ ,147.45
+ ,118.80
+ ,105.23
+ ,128.42
+ ,106.10
+ ,105.58
+ ,136.90
+ ,109.30
+ ,105.34
+ ,143.95
+ ,117.20
+ ,105.28
+ ,135.64
+ ,92.50
+ ,105.70
+ ,122.48
+ ,104.20
+ ,105.67
+ ,136.83
+ ,112.50
+ ,105.71
+ ,153.04
+ ,122.40
+ ,106.19
+ ,142.71
+ ,113.30
+ ,106.93
+ ,123.46
+ ,100.00
+ ,107.44
+ ,144.37
+ ,110.70
+ ,107.85
+ ,146.15
+ ,112.80
+ ,108.71
+ ,147.61
+ ,109.80
+ ,109.32
+ ,158.51
+ ,117.30
+ ,109.49
+ ,147.40
+ ,109.10
+ ,110.20
+ ,165.05
+ ,115.90
+ ,110.62
+ ,154.64
+ ,96.00
+ ,111.22
+ ,126.20
+ ,99.80
+ ,110.88
+ ,157.36
+ ,116.80
+ ,111.15
+ ,154.15
+ ,115.70
+ ,111.29
+ ,123.21
+ ,99.40
+ ,111.09
+ ,113.07
+ ,94.30
+ ,111.24
+ ,110.45
+ ,91.00
+ ,111.45
+ ,113.57
+ ,93.20
+ ,111.75
+ ,122.44
+ ,103.10
+ ,111.07
+ ,114.93
+ ,94.10
+ ,111.17
+ ,111.85
+ ,91.80
+ ,110.96
+ ,126.04
+ ,102.70
+ ,110.50
+ ,121.34
+ ,82.60
+ ,110.48
+ ,124.36
+ ,89.10
+ ,110.66)
+ ,dim=c(3
+ ,70)
+ ,dimnames=list(c('Uitvoer'
+ ,'TIP'
+ ,'cons')
+ ,1:70))
> y <- array(NA,dim=c(3,70),dimnames=list(c('Uitvoer','TIP','cons'),1:70))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Uitvoer TIP cons
1 103.34 98.6 96.33
2 102.60 96.9 96.33
3 100.69 95.1 95.05
4 105.67 97.0 96.84
5 123.61 112.7 96.92
6 113.08 102.9 97.44
7 106.46 97.4 97.78
8 123.38 111.4 97.69
9 109.87 87.4 96.67
10 95.74 96.8 98.29
11 123.06 114.1 98.20
12 123.39 110.3 98.71
13 120.28 103.9 98.54
14 115.33 101.6 98.20
15 110.40 94.6 100.80
16 114.49 95.9 101.33
17 132.03 104.7 101.88
18 123.16 102.8 101.85
19 118.82 98.1 102.04
20 128.32 113.9 102.22
21 112.24 80.9 102.63
22 104.53 95.7 102.65
23 132.57 113.2 102.54
24 122.52 105.9 102.37
25 131.80 108.8 102.68
26 124.55 102.3 102.76
27 120.96 99.0 102.82
28 122.60 100.7 103.31
29 145.52 115.5 103.23
30 118.57 100.7 103.60
31 134.25 109.9 103.95
32 136.70 114.6 103.93
33 121.37 85.4 104.25
34 111.63 100.5 104.38
35 134.42 114.8 104.36
36 137.65 116.5 104.32
37 137.86 112.9 104.58
38 119.77 102.0 104.68
39 130.69 106.0 104.92
40 128.28 105.3 105.46
41 147.45 118.8 105.23
42 128.42 106.1 105.58
43 136.90 109.3 105.34
44 143.95 117.2 105.28
45 135.64 92.5 105.70
46 122.48 104.2 105.67
47 136.83 112.5 105.71
48 153.04 122.4 106.19
49 142.71 113.3 106.93
50 123.46 100.0 107.44
51 144.37 110.7 107.85
52 146.15 112.8 108.71
53 147.61 109.8 109.32
54 158.51 117.3 109.49
55 147.40 109.1 110.20
56 165.05 115.9 110.62
57 154.64 96.0 111.22
58 126.20 99.8 110.88
59 157.36 116.8 111.15
60 154.15 115.7 111.29
61 123.21 99.4 111.09
62 113.07 94.3 111.24
63 110.45 91.0 111.45
64 113.57 93.2 111.75
65 122.44 103.1 111.07
66 114.93 94.1 111.17
67 111.85 91.8 110.96
68 126.04 102.7 110.50
69 121.34 82.6 110.48
70 124.36 89.1 110.66
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) TIP cons
-153.269 1.128 1.559
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.7350 -4.2722 -0.7397 2.7077 26.2410
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -153.2692 23.2305 -6.598 7.89e-09 ***
TIP 1.1281 0.1018 11.081 < 2e-16 ***
cons 1.5588 0.2013 7.745 6.93e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.006 on 67 degrees of freedom
Multiple R-squared: 0.7383, Adjusted R-squared: 0.7305
F-statistic: 94.5 on 2 and 67 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 1.296587e-02 0.0259317399 0.98703413
[2,] 2.236846e-03 0.0044736911 0.99776315
[3,] 3.502713e-04 0.0007005427 0.99964973
[4,] 1.276691e-01 0.2553382452 0.87233088
[5,] 3.328373e-01 0.6656746732 0.66716266
[6,] 2.407609e-01 0.4815217882 0.75923911
[7,] 1.841850e-01 0.3683699553 0.81581502
[8,] 1.568179e-01 0.3136357136 0.84318214
[9,] 1.079550e-01 0.2159099740 0.89204501
[10,] 6.790344e-02 0.1358068733 0.93209656
[11,] 4.205931e-02 0.0841186216 0.95794069
[12,] 4.980739e-02 0.0996147848 0.95019261
[13,] 3.002440e-02 0.0600488089 0.96997560
[14,] 1.753969e-02 0.0350793706 0.98246031
[15,] 1.438517e-02 0.0287703397 0.98561483
[16,] 1.567388e-02 0.0313477581 0.98432612
[17,] 5.365552e-02 0.1073110461 0.94634448
[18,] 3.577530e-02 0.0715506047 0.96422470
[19,] 2.441778e-02 0.0488355679 0.97558222
[20,] 1.681766e-02 0.0336353281 0.98318234
[21,] 1.028033e-02 0.0205606559 0.98971967
[22,] 6.019115e-03 0.0120382291 0.99398089
[23,] 3.409270e-03 0.0068185408 0.99659073
[24,] 4.806465e-03 0.0096129298 0.99519354
[25,] 3.683825e-03 0.0073676504 0.99631617
[26,] 2.093925e-03 0.0041878507 0.99790607
[27,] 1.168273e-03 0.0023365467 0.99883173
[28,] 3.226723e-03 0.0064534458 0.99677328
[29,] 1.038843e-02 0.0207768587 0.98961157
[30,] 7.380074e-03 0.0147601488 0.99261993
[31,] 4.888893e-03 0.0097777863 0.99511111
[32,] 2.968999e-03 0.0059379979 0.99703100
[33,] 2.678845e-03 0.0053576906 0.99732115
[34,] 1.520936e-03 0.0030418728 0.99847906
[35,] 9.105421e-04 0.0018210842 0.99908946
[36,] 6.290382e-04 0.0012580764 0.99937096
[37,] 4.021071e-04 0.0008042142 0.99959789
[38,] 2.218759e-04 0.0004437518 0.99977812
[39,] 1.315259e-04 0.0002630518 0.99986847
[40,] 2.111631e-03 0.0042232628 0.99788837
[41,] 2.093582e-03 0.0041871643 0.99790642
[42,] 1.283560e-03 0.0025671207 0.99871644
[43,] 9.498652e-04 0.0018997304 0.99905013
[44,] 5.654427e-04 0.0011308855 0.99943456
[45,] 5.673525e-04 0.0011347050 0.99943265
[46,] 3.663412e-04 0.0007326824 0.99963366
[47,] 2.872024e-04 0.0005744047 0.99971280
[48,] 1.719885e-04 0.0003439771 0.99982801
[49,] 1.410621e-04 0.0002821241 0.99985894
[50,] 6.875104e-05 0.0001375021 0.99993125
[51,] 1.257136e-04 0.0002514271 0.99987429
[52,] 1.495690e-01 0.2991380318 0.85043098
[53,] 1.532666e-01 0.3065332232 0.84673339
[54,] 1.925281e-01 0.3850562517 0.80747187
[55,] 9.253470e-01 0.1493059814 0.07465299
[56,] 9.270684e-01 0.1458632211 0.07293161
[57,] 9.083484e-01 0.1833032809 0.09165164
[58,] 8.627449e-01 0.2745101257 0.13725506
[59,] 8.233611e-01 0.3532778952 0.17663895
> postscript(file="/var/wessaorg/rcomp/tmp/1v7dq1356023537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2l3y31356023537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3mb351356023537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ukwa1356023537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5av8f1356023537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 70
Frequency = 1
1 2 3 4 5 6
-4.7820419 -3.6042009 -1.4883178 -1.4419888 -1.3385159 -1.6232882
7 8 9 10 11 12
-2.5684911 -1.3021860 13.8531632 -13.4065797 -5.4631424 -1.6411774
13 14 15 16 17 18
2.7339210 0.9086294 -0.1771853 1.6200813 8.3751094 1.6953419
19 20 21 22 23 24
2.3614414 -6.2437771 14.2658040 -10.1718692 -1.7028848 -3.2524589
25 26 27 28 29 30
2.2727108 2.2309303 2.2702718 1.2286326 7.5768365 -3.2534113
31 32 33 34 35 36
1.5021145 -1.3189762 15.7939557 -11.1836250 -4.4948765 -3.1203666
37 38 39 40 41 42
0.7456630 -5.2034692 0.8298586 -1.6321790 2.6664250 -2.5817450
43 44 45 46 47 48
2.6623066 0.8935132 19.7939301 -6.5185652 -1.5944925 2.6986937
49 50 51 52 53 54
1.4812924 -3.5593961 4.6403907 2.7107493 6.6043236 8.7782693
55 56 57 58 59 60
5.8123036 15.1362555 26.2410131 -5.9559431 5.6047788 3.4175067
61 62 63 64 65 66
-8.8220285 -13.4423214 -12.6667957 -12.4963391 -13.7349775 -11.2475790
67 68 69 70
-11.4055109 -8.7952208 9.2116036 4.6181034
> postscript(file="/var/wessaorg/rcomp/tmp/6pudx1356023537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 70
Frequency = 1
lag(myerror, k = 1) myerror
0 -4.7820419 NA
1 -3.6042009 -4.7820419
2 -1.4883178 -3.6042009
3 -1.4419888 -1.4883178
4 -1.3385159 -1.4419888
5 -1.6232882 -1.3385159
6 -2.5684911 -1.6232882
7 -1.3021860 -2.5684911
8 13.8531632 -1.3021860
9 -13.4065797 13.8531632
10 -5.4631424 -13.4065797
11 -1.6411774 -5.4631424
12 2.7339210 -1.6411774
13 0.9086294 2.7339210
14 -0.1771853 0.9086294
15 1.6200813 -0.1771853
16 8.3751094 1.6200813
17 1.6953419 8.3751094
18 2.3614414 1.6953419
19 -6.2437771 2.3614414
20 14.2658040 -6.2437771
21 -10.1718692 14.2658040
22 -1.7028848 -10.1718692
23 -3.2524589 -1.7028848
24 2.2727108 -3.2524589
25 2.2309303 2.2727108
26 2.2702718 2.2309303
27 1.2286326 2.2702718
28 7.5768365 1.2286326
29 -3.2534113 7.5768365
30 1.5021145 -3.2534113
31 -1.3189762 1.5021145
32 15.7939557 -1.3189762
33 -11.1836250 15.7939557
34 -4.4948765 -11.1836250
35 -3.1203666 -4.4948765
36 0.7456630 -3.1203666
37 -5.2034692 0.7456630
38 0.8298586 -5.2034692
39 -1.6321790 0.8298586
40 2.6664250 -1.6321790
41 -2.5817450 2.6664250
42 2.6623066 -2.5817450
43 0.8935132 2.6623066
44 19.7939301 0.8935132
45 -6.5185652 19.7939301
46 -1.5944925 -6.5185652
47 2.6986937 -1.5944925
48 1.4812924 2.6986937
49 -3.5593961 1.4812924
50 4.6403907 -3.5593961
51 2.7107493 4.6403907
52 6.6043236 2.7107493
53 8.7782693 6.6043236
54 5.8123036 8.7782693
55 15.1362555 5.8123036
56 26.2410131 15.1362555
57 -5.9559431 26.2410131
58 5.6047788 -5.9559431
59 3.4175067 5.6047788
60 -8.8220285 3.4175067
61 -13.4423214 -8.8220285
62 -12.6667957 -13.4423214
63 -12.4963391 -12.6667957
64 -13.7349775 -12.4963391
65 -11.2475790 -13.7349775
66 -11.4055109 -11.2475790
67 -8.7952208 -11.4055109
68 9.2116036 -8.7952208
69 4.6181034 9.2116036
70 NA 4.6181034
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.6042009 -4.7820419
[2,] -1.4883178 -3.6042009
[3,] -1.4419888 -1.4883178
[4,] -1.3385159 -1.4419888
[5,] -1.6232882 -1.3385159
[6,] -2.5684911 -1.6232882
[7,] -1.3021860 -2.5684911
[8,] 13.8531632 -1.3021860
[9,] -13.4065797 13.8531632
[10,] -5.4631424 -13.4065797
[11,] -1.6411774 -5.4631424
[12,] 2.7339210 -1.6411774
[13,] 0.9086294 2.7339210
[14,] -0.1771853 0.9086294
[15,] 1.6200813 -0.1771853
[16,] 8.3751094 1.6200813
[17,] 1.6953419 8.3751094
[18,] 2.3614414 1.6953419
[19,] -6.2437771 2.3614414
[20,] 14.2658040 -6.2437771
[21,] -10.1718692 14.2658040
[22,] -1.7028848 -10.1718692
[23,] -3.2524589 -1.7028848
[24,] 2.2727108 -3.2524589
[25,] 2.2309303 2.2727108
[26,] 2.2702718 2.2309303
[27,] 1.2286326 2.2702718
[28,] 7.5768365 1.2286326
[29,] -3.2534113 7.5768365
[30,] 1.5021145 -3.2534113
[31,] -1.3189762 1.5021145
[32,] 15.7939557 -1.3189762
[33,] -11.1836250 15.7939557
[34,] -4.4948765 -11.1836250
[35,] -3.1203666 -4.4948765
[36,] 0.7456630 -3.1203666
[37,] -5.2034692 0.7456630
[38,] 0.8298586 -5.2034692
[39,] -1.6321790 0.8298586
[40,] 2.6664250 -1.6321790
[41,] -2.5817450 2.6664250
[42,] 2.6623066 -2.5817450
[43,] 0.8935132 2.6623066
[44,] 19.7939301 0.8935132
[45,] -6.5185652 19.7939301
[46,] -1.5944925 -6.5185652
[47,] 2.6986937 -1.5944925
[48,] 1.4812924 2.6986937
[49,] -3.5593961 1.4812924
[50,] 4.6403907 -3.5593961
[51,] 2.7107493 4.6403907
[52,] 6.6043236 2.7107493
[53,] 8.7782693 6.6043236
[54,] 5.8123036 8.7782693
[55,] 15.1362555 5.8123036
[56,] 26.2410131 15.1362555
[57,] -5.9559431 26.2410131
[58,] 5.6047788 -5.9559431
[59,] 3.4175067 5.6047788
[60,] -8.8220285 3.4175067
[61,] -13.4423214 -8.8220285
[62,] -12.6667957 -13.4423214
[63,] -12.4963391 -12.6667957
[64,] -13.7349775 -12.4963391
[65,] -11.2475790 -13.7349775
[66,] -11.4055109 -11.2475790
[67,] -8.7952208 -11.4055109
[68,] 9.2116036 -8.7952208
[69,] 4.6181034 9.2116036
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.6042009 -4.7820419
2 -1.4883178 -3.6042009
3 -1.4419888 -1.4883178
4 -1.3385159 -1.4419888
5 -1.6232882 -1.3385159
6 -2.5684911 -1.6232882
7 -1.3021860 -2.5684911
8 13.8531632 -1.3021860
9 -13.4065797 13.8531632
10 -5.4631424 -13.4065797
11 -1.6411774 -5.4631424
12 2.7339210 -1.6411774
13 0.9086294 2.7339210
14 -0.1771853 0.9086294
15 1.6200813 -0.1771853
16 8.3751094 1.6200813
17 1.6953419 8.3751094
18 2.3614414 1.6953419
19 -6.2437771 2.3614414
20 14.2658040 -6.2437771
21 -10.1718692 14.2658040
22 -1.7028848 -10.1718692
23 -3.2524589 -1.7028848
24 2.2727108 -3.2524589
25 2.2309303 2.2727108
26 2.2702718 2.2309303
27 1.2286326 2.2702718
28 7.5768365 1.2286326
29 -3.2534113 7.5768365
30 1.5021145 -3.2534113
31 -1.3189762 1.5021145
32 15.7939557 -1.3189762
33 -11.1836250 15.7939557
34 -4.4948765 -11.1836250
35 -3.1203666 -4.4948765
36 0.7456630 -3.1203666
37 -5.2034692 0.7456630
38 0.8298586 -5.2034692
39 -1.6321790 0.8298586
40 2.6664250 -1.6321790
41 -2.5817450 2.6664250
42 2.6623066 -2.5817450
43 0.8935132 2.6623066
44 19.7939301 0.8935132
45 -6.5185652 19.7939301
46 -1.5944925 -6.5185652
47 2.6986937 -1.5944925
48 1.4812924 2.6986937
49 -3.5593961 1.4812924
50 4.6403907 -3.5593961
51 2.7107493 4.6403907
52 6.6043236 2.7107493
53 8.7782693 6.6043236
54 5.8123036 8.7782693
55 15.1362555 5.8123036
56 26.2410131 15.1362555
57 -5.9559431 26.2410131
58 5.6047788 -5.9559431
59 3.4175067 5.6047788
60 -8.8220285 3.4175067
61 -13.4423214 -8.8220285
62 -12.6667957 -13.4423214
63 -12.4963391 -12.6667957
64 -13.7349775 -12.4963391
65 -11.2475790 -13.7349775
66 -11.4055109 -11.2475790
67 -8.7952208 -11.4055109
68 9.2116036 -8.7952208
69 4.6181034 9.2116036
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7nssi1356023537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/84x1x1356023537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/91hvg1356023537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/104fxk1356023537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/115s3v1356023537.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/128ck91356023537.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13y8s61356023537.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14bzve1356023537.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15dvuf1356023537.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16j4hw1356023537.tab")
+ }
>
> try(system("convert tmp/1v7dq1356023537.ps tmp/1v7dq1356023537.png",intern=TRUE))
character(0)
> try(system("convert tmp/2l3y31356023537.ps tmp/2l3y31356023537.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mb351356023537.ps tmp/3mb351356023537.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ukwa1356023537.ps tmp/4ukwa1356023537.png",intern=TRUE))
character(0)
> try(system("convert tmp/5av8f1356023537.ps tmp/5av8f1356023537.png",intern=TRUE))
character(0)
> try(system("convert tmp/6pudx1356023537.ps tmp/6pudx1356023537.png",intern=TRUE))
character(0)
> try(system("convert tmp/7nssi1356023537.ps tmp/7nssi1356023537.png",intern=TRUE))
character(0)
> try(system("convert tmp/84x1x1356023537.ps tmp/84x1x1356023537.png",intern=TRUE))
character(0)
> try(system("convert tmp/91hvg1356023537.ps tmp/91hvg1356023537.png",intern=TRUE))
character(0)
> try(system("convert tmp/104fxk1356023537.ps tmp/104fxk1356023537.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.285 1.168 7.512